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Zhang L, Liu J, Cao Y, Liu S, Zhao W, Wang C, Banzhao S, Liu Z, Liu L. Association between circulating levels of unsaturated fatty acids and risk for prediabetes in the NHANES 2003-2004 and 2011-2012. Diabetes Res Clin Pract 2024; 213:111728. [PMID: 38838943 DOI: 10.1016/j.diabres.2024.111728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/11/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
AIMS This study aimed to investigate the association between serum levels of common and uncommon unsaturated fatty acids and prediabetes risk. METHODS Data were collected from the National Health and Nutrition Examination Survey for 2003-2004 and 2011-2012. Weighted proportional and multivariate logistic regression analyses were performed to assess the association of serum PUFAs and MUFAs with prediabetes risk after adjusting for potential confounders. RESULTS A total of 3575 individuals were enrolled in this study. Serum levels of PUFAs EPA (20:5 n3) and GLA (18:3 n6) were associated with increased prediabetes risk (EPA (20:5 n3): OR = 1.878, 95% CI: 1.177-2.996, Ptrend = 0.002; GLA (18:3 n6): 1.702, 95% CI: 1.140-2.541, Ptrend = 0.016). The MUFAs PA (16:1 n7) and EA (20:1 n9) were associated with the risk of prediabetes (OR in quintile5: PA (16:1 n7): 1.780, 95% CI: 1.056-3.001, Ptrend = 0.003; EA (20:1 n9): 0.587, 95% CI: 0.347-0.994, Ptrend = 0.010). Moreover, nonlinear analysis revealed that serum levels of EPA (20:5 n3) and EA (20:1 n-9) were nonlinearly associated with prediabetes risk. CONCLUSION Some serum n-3 PUFAs are positively associated with prediabetes, several serum n-6 PUFAs are inversely associated with prediabetes. Regulating individual serum USFA levels may help prevent prediabetes, thereby providing evidence for clinical and nutritional practices.
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Affiliation(s)
- Liwen Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Jiayi Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Yuxuan Cao
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Shan Liu
- Department of Endocrinology, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, China
| | - Weili Zhao
- Hebei Key Laboratory of Basic Medicine for Diabetes, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, China
| | - Ci Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Shangfang Banzhao
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Zanchao Liu
- Hebei Key Laboratory of Basic Medicine for Diabetes, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, China; Shijiazhuang Diabetes Precision Diagnosis and Treatment Technology Innovation Center, Shijiazhuang, Hebei 050000, China.
| | - Lipeng Liu
- Hebei Key Laboratory of Basic Medicine for Diabetes, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, China; College of Veterinary, Hebei Agricultural University, Baoding 071000, China.
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Cao L, Wen Y, Fan K, Wang Q, Zhang Y, Li Z, Wang N, Zhang X. Association of birth weight with type 2 diabetes mellitus and the mediating role of fatty acids traits: a two-step mendelian randomization study. Lipids Health Dis 2024; 23:97. [PMID: 38566047 PMCID: PMC10986016 DOI: 10.1186/s12944-024-02087-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Observational studies have suggested an association between birth weight and type 2 diabetes mellitus, but the causality between them has not been established. We aimed to obtain the causal relationship between birth weight with T2DM and quantify the mediating effects of potential modifiable risk factors. METHODS Two-step, two-sample Mendelian randomization (MR) techniques were applied using SNPs as genetic instruments for exposure and mediators. Summary data from genome-wide association studies (GWAS) for birth weight, T2DM, and a series of fatty acids traits and their ratios were leveraged. The inverse variance weighted (IVW) method was the main analysis approach. In addition, the heterogeneity test, horizontal pleiotropy test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, and leave-one-out analysis were carried out to assess the robustness. RESULTS The IVW method showed that lower birth weight raised the risk of T2DM (β: -1.113, 95% CI: -1.573 ∼ -0.652). Two-step MR identified 4 of 17 candidate mediators partially mediating the effect of lower birth weight on T2DM, including ratio of polyunsaturated fatty acids to monounsaturated fatty acids (proportion mediated: 7.9%), ratio of polyunsaturated fatty acids to total fatty acids (7.2%), ratio of omega-6 fatty acids to total fatty acids (8.1%) and ratio of linoleic acid to total fatty acids ratio (6.0%). CONCLUSIONS Our findings supported a potentially causal effect of birth weight against T2DM with considerable mediation by modifiable risk factors. Interventions that target these factors have the potential to reduce the burden of T2DM attributable to low birth weight.
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Affiliation(s)
- Limin Cao
- Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital), Xinmin North Street No.13, Taiyuan, Shanxi, China
| | - Yahui Wen
- Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital), Xinmin North Street No.13, Taiyuan, Shanxi, China
| | - Keyi Fan
- Shanxi Medical University, Taiyuan, China
| | - Qiwei Wang
- Shanxi Medical University, Taiyuan, China
| | | | - Zhenglong Li
- Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital), Xinmin North Street No.13, Taiyuan, Shanxi, China
| | - Nan Wang
- Shanxi Medical University, Taiyuan, China
| | - Xinhua Zhang
- Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital), Xinmin North Street No.13, Taiyuan, Shanxi, China.
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Chen X, Li P, Huang Y, Lv Y, Xu X, Nong H, Zhang L, Wu H, Yu C, Chen L, Liu D, Wei L, Zhang H. Joint associations among non-essential heavy metal mixtures and nutritional factors on glucose metabolism indexes in US adults: evidence from the NHANES 2011-2016. Food Funct 2024; 15:2706-2718. [PMID: 38376466 DOI: 10.1039/d3fo05439j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Dietary intake can modify the impact of metals on human health, and is also closely related to glucose metabolism in human bodies. However, research on their interaction is limited. We used data based on 1738 adults aged ≥20 years from the National Health and Nutrition Examination Survey 2011-2016. We combined linear regression and restricted cubic splines with Bayesian kernel machine regression (BKMR) to identify metals associated with each glucose metabolism index (P < 0.05 and the posterior inclusion probabilities of BKMR >0.5) in eight non-essential heavy metals (barium, cadmium, antimony, tungsten, uranium, arsenic, lead, and thallium) and glucose metabolism indexes [fasting plasma glucose (FPG), blood hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)]. We identified two pairs of metals associated with glucose metabolism indexes: cadmium and tungsten to HbA1c and barium and thallium to HOMA-IR. Then, the cross-validated kernel ensemble (CVEK) approach was applied to identify the specific nutrient group (nutrients) that interacted with the association. By using the CVEK model, we identified significant interactions between the energy-adjusted diet inflammatory index (E-DII) and cadmium, tungsten and barium (all P < 0.05); macro-nutrients and cadmium, tungsten and barium (all P < 0.05); minerals and cadmium, tungsten, barium and thallium (all P < 0.05); and A vitamins and thallium (P = 0.043). Furthermore, a lower E-DII, a lower intake of carbohydrates and phosphorus, and a higher consumption of magnesium seem to attenuate the positive association between metals and glucose metabolism indexes. Our finding identifying the nutrients that interact with non-essential heavy metals could provide a feasible nutritional guideline for the general population to protect against the adverse effects of non-essential heavy metals on glucose metabolism.
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Affiliation(s)
- Xiaolang Chen
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Peipei Li
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Yuanhao Huang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Yingnan Lv
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Xia Xu
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Huiyun Nong
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Lulu Zhang
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Huabei Wu
- School of General Practice, Guangxi Medical University, Nanning 530021, China
| | - Chao Yu
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Lina Chen
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Di Liu
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Lancheng Wei
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
| | - Haiying Zhang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning 530021, China.
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning 530021, China
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, Guangxi, China
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Liu W, Zhu M, Liu J, Su S, Zeng X, Fu F, Lu Y, Rao Z, Chen Y. Comparison of the effects of monounsaturated fatty acids and polyunsaturated fatty acids on the lipotoxicity of islets. Front Endocrinol (Lausanne) 2024; 15:1368853. [PMID: 38501107 PMCID: PMC10945794 DOI: 10.3389/fendo.2024.1368853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Background Monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) have been reported to combat saturated fatty acid (SFA)-induced cellular damage, however, their clinical effects on patients with metabolic diseases such as diabetes and hyperlipidemia are still controversial. Since comparative studies of the effects of these two types of unsaturated fatty acids (UFAs) are still limited. In this study, we aimed to compare the protective effects of various UFAs on pancreatic islets under the stress of SFA-induced metabolic disorder and lipotoxicity. Methods Rat insulinoma cell line INS-1E were treated with palmitic acid (PA) with or without UFAs including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), arachidonic acid (AA), and oleic acid (OA) to determine cell viability, apoptosis, endoplasmic reticulum (ER) stress, and inflammatory. In vivo, male C57BL/6 mice were fed a 60% high-fat diet (HFD) for 12 w. Then the lard in HFD was partially replaced with fish oil (FO) and olive oil (OO) at low or high proportions of energy (5% or 20%) to observe the ameliorative effects of the UFA supplement. Results All UFAs significantly improved PA-induced cell viability impairment in INS-1E cells, and their alleviation on PA induced apoptosis, ER stress and inflammation were confirmed. Particularly, OA had better effects than EPA, DHA, and AA on attenuating cellular ER stress. In vivo, the diets with a low proportion of UFAs (5% of energy) had limited effects on HFD induced metabolic disorder, except for a slight improved intraperitoneal glucose tolerance in obese mice. However, when fed diets containing a high proportion of UFAs (20% of energy), both the FO and OO groups exhibited substantially improved glucose and lipid metabolism, such as decrease in total cholesterol (TC), low-density lipoprotein (LDL), fasting blood glucose (FBG), and fasting blood insulin (FBI)) and improvement of insulin sensitivity evidenced by intraperitoneal glucose tolerance test (IPGTT) and intraperitoneal insulin tolerance test (IPITT). Unexpectedly, FO resulted in abnormal elevation of the liver function index aspartate aminotransferase (AST) in serum. Pathologically, OO attenuated HFD-induced compensatory hyperplasia of pancreatic islets, while this effect was not obvious in the FO group. Conclusions Both MUFAs and PUFAs can effectively protect islet β cells from SFA-induced cellular lipotoxicity. In particular, both OA in vitro and OO in vivo showed superior activities on protecting islets function and enhance insulin sensitivity, suggesting that MUFAs might have greater potential for nutritional intervention on diabetes.
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Affiliation(s)
- Wen Liu
- Department of Clinical Nutrition and Key Laboratory of Transplant Engineering and Immunology, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Min Zhu
- Department of Clinical Nutrition and Key Laboratory of Transplant Engineering and Immunology, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jingyi Liu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Shan Su
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Zeng
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Fudong Fu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yanrong Lu
- Department of Clinical Nutrition and Key Laboratory of Transplant Engineering and Immunology, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiyong Rao
- Department of Clinical Nutrition, West China Hospital, Sichuan University, Chengdu, China
| | - Younan Chen
- Department of Clinical Nutrition and Key Laboratory of Transplant Engineering and Immunology, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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Goodrich JA, Wang H, Walker DI, Lin X, Hu X, Alderete TL, Chen Z, Valvi D, Baumert BO, Rock S, Berhane K, Gilliland FD, Goran MI, Jones DP, Conti DV, Chatzi L. Postprandial Metabolite Profiles and Risk of Prediabetes in Young People: A Longitudinal Multicohort Study. Diabetes Care 2024; 47:151-159. [PMID: 37971952 PMCID: PMC10733648 DOI: 10.2337/dc23-0327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 10/22/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Prediabetes in young people is an emerging epidemic that disproportionately impacts Hispanic populations. We aimed to develop a metabolite-based prediction model for prediabetes in young people with overweight/obesity at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS In independent, prospective cohorts of Hispanic youth (discovery; n = 143 without baseline prediabetes) and predominately Hispanic young adults (validation; n = 56 without baseline prediabetes), we assessed prediabetes via 2-h oral glucose tolerance tests. Baseline metabolite levels were measured in plasma from a 2-h postglucose challenge. In the discovery cohort, least absolute shrinkage and selection operator regression with a stability selection procedure was used to identify robust predictive metabolites for prediabetes. Predictive performance was evaluated in the discovery and validation cohorts using logistic regression. RESULTS Two metabolites (allylphenol sulfate and caprylic acid) were found to predict prediabetes beyond known risk factors, including sex, BMI, age, ethnicity, fasting/2-h glucose, total cholesterol, and triglycerides. In the discovery cohort, the area under the receiver operator characteristic curve (AUC) of the model with metabolites and known risk factors was 0.80 (95% CI 0.72-0.87), which was higher than the risk factor-only model (AUC 0.63 [0.53-0.73]; P = 0.001). When the predictive models developed in the discovery cohort were applied to the replication cohort, the model with metabolites and risk factors predicted prediabetes more accurately (AUC 0.70 [95% CI 40.55-0.86]) than the same model without metabolites (AUC 0.62 [0.46-0.79]). CONCLUSIONS Metabolite profiles may help improve prediabetes prediction compared with traditional risk factors. Findings suggest that medium-chain fatty acids and phytochemicals are early indicators of prediabetes in high-risk youth.
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Affiliation(s)
- Jesse A. Goodrich
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Hongxu Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Douglas I. Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Xiangping Lin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Xin Hu
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA
| | - Tanya L. Alderete
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Damaskini Valvi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brittney O. Baumert
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Sarah Rock
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Kiros Berhane
- Department of Biostatistics, Columbia University, New York, NY
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Michael I. Goran
- Division of Endocrinology, Department of Pediatrics, Saban Research Institute, Children’s Hospital Los Angeles, Los Angeles, CA
- Department of Pediatrics, Keck School of Medicine, Los Angeles, CA
| | - Dean P. Jones
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA
| | - David V. Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Leda Chatzi
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
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